Spring

There are some neurologic related diseases that might be seen in the same person and whether there are comman factors that lead to the situation or not has been the subject of interest in biomedical science. Here, by using text mining with articles, our aim is to propose a connection between diseases in gene base. In this project, spesifically, we focussed on the connection between Autism Spectrum Disorder & Epilepsy Disorder and derivatives because they often occur together. We have used PubMed to retrieve related articles and created gene-gene graphs with them.

We propose to develop a machine learning system which can predict which home users of an Internet Service Provider will call to within one week. Training data for the system will be provided via per-minute data gathered from modems by the Lifemote, labeled both manually and by the ISP call center via the Lifemote user interface. Roughly 3 percent of users call their ISP to complain each month. The goal of the project is identify the 10 percent of the population most likely to call with a false negative rate of less than 20 percent.

A new neural network architecture to extract key frames is proposed. We combine ideas from both Learning to Explain: An Information-Theoretic Perspective on Model Interpretation, which is a work on instance-wise feature selection, and Concrete Autoencoders for Differentiable Feature Selection and Reconstruction, which is our previous work on unsupervised feature selection. Experiments are conducted on the BosphorusSign dataset, classification and reconstruction errors of the selected key frames are measured.

Wi-Fi standard on 2.4Ghz ISM band specifies 22MHz wide channels seperated by 5MHz increments. With this scheme adjacent channels overlap and devices operating on adjacent channels interfere with each other. Side channel interference results in a drop on communication rates and reduces the Wi-Fi performance. Most of the time it's not possible to find out which channel the interference is coming from. In test environments we have generated Wi-Fi traffic on all adjacent channels and made observations on the impacts of each adjacent channel.

User stories are one of the primary development artifacts for Scrum and Extreme Programming (XP) project teams. Visualizing user stories in meaningful and interactive views is an important effort enhancing the communication in a project team. The project aims to provide visual representation for user stories so that they can be more intuitively examined and understood in the workplace. The project is also an attempt to find out the best practices while visualizing user stories.